Effectively learning from noisy labels is becoming an essential need of Deep Learning applications.
In order to gather the massive amounts of annotations required to train Deep Learning models, researchers have recently been looking for alternatives to standard in-house annotation, such as crowd-sourcing ([1]}) or automatic-labelling ([2]}). However, while dramatically cutting down the cost of annotations, these approaches tend to provide noisy labels. In order to tackle the learning-from-noisy-labels problem, several approaches have been proposed in literature. Following [3]}, state-of-the-art approaches can be categorized in four groups. Robust Architecture methods involve architectural modifications of standard neural networks during training, for example by adding a noise adaptation layer to model the label transition matrix of a noisy dataset ([4]}). Robust Regularization approaches involve the use of techniques such as data augmentation, weight decay, dropout, and batch normalization to prevent the overfitting of the corrupted examples. Robust Loss Design approaches involve the modifications of standard loss functions to make them noise tolerant. Examples include generalized cross entropy (GCE, [5]}), symmetric cross entropy (SCE, [6]}) and active passive loss (APL, [7]}). Finally, Sample Selection approaches, propose strategies to select well-labelled samples. A popular approach for sample selection is multi-network training: MentorNet ([8]}) uses a mentor network, pre-trained on clean labels, in order to provide a curriculum for the training of a Student network. Coteaching ([9]}) selects probably well-labelled samples according to a small-loss trick, training two neural-networks in a collaborative way. While well theoretically motivated, the effectiveness of the above mentioned methods has been proven mainly in the classification task for simpler datasets than the surgical ones, such as artificially modified versions of benchmark datasets like CIFAR ([10]}), MNIST ([11]}) and FASHION MNIST ([12]}), and, less frequently, in real-world datasets with modest-to-medium amount of noise like ANIMAL 10-N ([13]}) (\(\approx 8.0\%\)  noise rate), Food 101-N ([14]}) (\(\approx 18.4\%\)  noise rate), WebVision ([15]}) (\(\approx 20.0\%\)  noise rate) and Clothing 1M ([16]}) (\(\approx 38.5\%\)  noise rate). Segmentation differs from standard classification since pixel-labels come grouped in images. This creates the need to rethink standard methods such as Sample-Selection, since discarding full labels may represent a waste of useful information. For this reason, local confidence map estimators have been proposed. In the context of 3D medical image segmentation, [17]} propose an approach for semi-supervised learning, where a segmentation model is first trained on clean labels, and then used to produce (noisy) pseudo-labels from unlabelled data, as well as confidence estimations via Monte-Carlo Dropout sampling, in order to train a Student model only on well-labelled regions of the pseudo-labels. [18]} also train a segmentation model on clean labels and, in parallel, a confidence model, implemented as a discriminator, in order to discriminate between predicted masks and ground-truth masks, by outputting pixel-wise scores. Unlabelled data can then be fed to segmentation and confidence models to predict pseudo-labels and local confidence maps, enriching the set of labelled training data only with high confidence regions of such predictions. While these methods have achieved good results, their effectiveness is still influenced by the amount and the quality of the available clean labels.
